Clinical Autonomy Research Articles

BackgroundThe physiotherapy and rehabilitation process in Türkiye is carried out by “specialist physicians” and “physiotherapists”. This phenomenological qualitative research aimed to explore how health professions education, particularly that of physiotherapists and referring physicians, shapes the physiotherapy process in Türkiye, through the experiences and perspectives of key stakeholders.MethodsA purposive sampling method was employed to select the study group. Data were collected from a heterogeneous group of 12 individuals with extensive experience related to the research phenomenon. Semi-structured in-depth interviews were used for data collection. Verified transcripts were analyzed using an inductive approach that involved content, thematic, and descriptive analyses. The NVivo 10 software program was used for qualitative analysis.ResultsThe analyses identified three main themes with subthemes. A total of 140 meaningful expressions were coded. The main themes were identified as “independent practice,” “education,” and “treatment process.” Despite physiotherapists’ competence in managing treatment for diagnosed patients, the current system does not allow such practices. Furthermore, significant criticisms have been identified regarding the inadequacy of physiotherapy education policies.ConclusionThe study reveals that the educational foundations of both physiotherapists and physicians significantly shape the physiotherapy process in Türkiye. Although physiotherapists receive extensive undergraduate training, inconsistencies in the quality of education and regulatory restrictions limit their clinical autonomy, impacting the efficiency of care delivery. Additionally, insufficient interdisciplinary training among physicians may hinder the implementation of collaborative rehabilitation practices. Enhancing the consistency, specialization, and integration of health professions education is essential for improving physiotherapy service quality and patient outcomes in Türkiye.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12909-025-08104-8.

This article examines the role of structured mentorship as a system-forming mechanism for accelerating novice orthodontists’ adaptation to clinical practice. The topic’s relevance stems from the widening gap between academic training and the exigencies of the real clinical milieu, where early-career practitioners experience cognitive, organizational, and affective overload. The absence of standardized accompaniment models delays the development of clinical autonomy and heightens the risk of error, rendering the search for a pedagogically validated, organizationally reproducible adaptation model acutely significant for contemporary dental training. The objective of the study is to determine the pedagogical, organizational, and psychological effects of structured mentorship and to justify its place within the architecture of the orthodontist’s professional formation. At the empirical level, an analysis was conducted of 17 sources published from 2019 to 2025, including systematic reviews, randomized controlled trials, and dental school reports. The novelty of the inquiry lies in integrating pedagogical theories and clinical metrics into a unified model of accelerated adaptation for residents, wherein structured mentorship functions not as an auxiliary but as the core technology of the educational process. The principal findings demonstrate that structured mentorship condenses the process of clinical formation, equalizes training quality, enhances interactional satisfaction, and improves the predictability of clinical outcomes. Sustainable implementation requires institutional preconditions: aligning the clinic’s value framework, appointing a coordinator, allocating time and resources, and cultivating a safe-to-err environment that ensures psychological safety for participants. The article will be useful to dental clinic leaders, medical education organizers, residents, and researchers in medical pedagogy seeking to improve the efficiency of early-career adaptation.

Corporate Practice/Dental Service Organization.

South Africa’s private healthcare sector is experiencing rapid transformation, driven by regulatory reform, digitalisation, and changing patient expectations. Independent medical practitioners must now balance clinical autonomy with financial sustainability amid growing systemic and market pressures. This study investigates how private practices in Cape Town adapt to these challenges through strategic and entrepreneurial innovation. Using a qualitative, interpretivist approach, the research applied the STOF (service, technology, organisation, and finance) business model framework to analyse practitioner experiences and adaptive responses. Seven interrelated domains emerged, highlighting financial fragility, operational inefficiency, and inconsistent technological integration. In response, two context-specific business models, Strategic Alliance Practitioner (SAP) and Consolidated Business Administration Services (CBAS), were conceptualised to strengthen value creation, operational efficiency, and long-term resilience. The findings demonstrate that practitioner-driven business model innovations can transform independent practices into sustainable, patient-centered enterprises. Moreover, the study emphasises the value of integrating policy, education, and professional development to build adaptable, resilient healthcare ecosystems suited to emerging market realities.

BackgroundHealth technology assessment (HTA) is a critical tool for evidence-based decision-making in healthcare systems, yet its implementation in low- and middle-income countries such as Iran remains understudied. This study examines the political dynamics of HTA implementation in Iran, focussing on the roles, interests and interactions of key stakeholders. By addressing the gap in understanding the political challenges and opportunities associated with HTA implementation, this study aims to provide actionable insights for policymakers and practitioners.MethodsA qualitative study design was employed, using in-depth semi-structured interviews with 19 stakeholders from 6 categories: interest groups, political leaders, donors, financial decision-makers, beneficiaries and bureaucracies. Participants were selected through purposive sampling to ensure representation across sectors. Data were analysed using thematic analysis, guided by the political analysis framework of Campos and Reich. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist to ensure methodological rigour.ResultsThe findings reveal significant barriers to HTA implementation in Iran, including limited political will, competing priorities, inter-agency coordination challenges and concerns about equity and access. Interest groups, such as physicians and pharmacists, expressed concerns about restricted clinical autonomy and limited access to innovative treatments. Political leaders emphasized the difficulty of prioritizing HTA amidst competing healthcare and economic issues, whilst donors highlighted the need for alignment with national priorities and long-term sustainability. Financial decision-makers acknowledged the potential of HTA to improve resource allocation but raised concerns about budgetary constraints. Beneficiaries stressed the importance of transparency and inclusion, and bureaucrats underscored the need for stronger leadership and capacity building.ConclusionsThe study underscores the importance of strong political leadership, stakeholder engagement and institutional capacity building for successful HTA implementation in Iran. Practical steps include strengthening inter-agency coordination mechanisms, ensuring transparent and inclusive decision-making processes, aligning donor support with national health priorities and investing in training programs to build technical capacity within government agencies. By addressing these challenges, policymakers can enhance the integration of HTA into the health system, ensuring efficient and equitable resource allocation.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12961-025-01400-1.

AI chatbots have introduced a new dimension to how we provide healthcare and support healthcare clinicians. However, despite the benefits of chatbots, their adoption raises ethical concerns related to the effects on users. This study discusses the ethical implications of psychological dependency on autonomy and decision-making in the context of healthcare delivery. Ten studies were analysed, revealing a cascading hierarchy involving the interconnected risks of threats to autonomy, disruption of critical thinking due to over-reliance, and psychological dependency, as well as issues of bias and misinformation in chatbot outputs, limitations in trust and reliability, and a mixed impact on clinicians' well-being. These findings underscore the importance of adopting a balanced approach to integrating AI chatbots into clinical practice, with a strong emphasis on preserving clinical autonomy to maintain the overall well-being of healthcare practitioners.

This study explores neonatal intensive care unit (NICU) nurses' perceptions of artificial intelligence (AI)-assisted neonatal skin assessment, focusing on its benefits, challenges, and ethical implications. Optimizing AI integration requires understanding nurses' attitudes. A qualitative phenomenological approach was employed. Semi-structured interviews were conducted with 23 NICU nurses from a public hospital in Gaziantep, Turkey, between January and March 2025. Data were analyzed using inductive content analysis to identify emerging themes related to AI's impact on clinical decision-making, workflow efficiency, and professional autonomy. Findings revealed that nurses acknowledged AI's potential to enhance diagnostic accuracy, standardize assessments, and reduce interobserver variability. However, concerns were raised regarding algorithm reliability, professional autonomy, and ethical considerations. Nurses recognized AI's potential but stressed the need for transparency, training, and safeguards against over-reliance. Participants emphasized human oversight to ensure patient-centered care. Artificial intelligence may improve neonatal skin assessment, but integration must balance technology and ethics. Engaging NICU nurses in AI system development and implementation is essential to fostering trust and ensuring alignment with clinical needs. Future research should assess AI's long-term impact and support interdisciplinary tool development that complements nursing expertise.

Genetic testing for hereditary cancer syndromes can provide lifesaving information allowing for individualized cancer screening, prevention, and treatment. A broader understanding of how psychosocial factors impact motivation to undergo genetic testing is needed to improve uptake among individuals who would benefit from testing. Adults (≥18 years) who met criteria for genetic testing based on a self-reported family cancer history and had not previously completed testing were invited to complete a survey (n = 799) assessing psychosocial factors including barriers to genetic testing, healthcare distrust, perceived self-efficacy, clinician autonomy support, and genetic testing knowledge. Associations between these psychosocial factors and testing motivation were examined first by correlation followed by multivariable linear regression. Self-efficacy had a significant positive correlation with genetic testing motivation, while barriers and healthcare distrust were negatively correlated with motivation. In an adjusted multivariable regression model, higher self-efficacy was associated with higher motivation while higher barriers and healthcare distrust were negatively associated with genetic testing motivation. Individuals of older age (51+), non-White race, and lower perceived socioeconomic status reported higher mean motivation scores. The negative association between distrust and barriers with genetic testing intention may be a potential target for tailored interventions for genetic testing.

BackgroundMental healthcare faces growing challenges due to rising mental health issues, particularly among young adults. AI-based systems show promise in supporting prevention, diagnosis, and treatment through personalized care but raise concerns about trust, inclusivity, and workflow integration. Limited research exists on aligning AI functionalities with healthcare professionals’ needs or incorporating shared decision-making (SDM) into AI-supported mental health services, emphasizing the need for further exploration.ObjectiveThis study aims to explore how AI-based decision support systems can be used in mental healthcare from the perspective of healthcare professionals and in the light of a SDM framework.MethodsA qualitative approach using deductive content analysis was employed. Sixteen healthcare professionals working with young adults participated in semi-structured interviews. The analysis was guided by elements of SDM to identify key needs and concerns related to AI.ResultsHealthcare professionals acknowledged both the potential benefits and challenges of integrating AI-based decision support systems into SDM for mental healthcare. Fifteen of 23 SDM elements were identified as relevant. AI was valued for its potential in early detection, holistic assessments, and personalized treatment recommendations. However, concerns were raised about inaccuracies in interpreting non-verbal cues, risks of overdiagnosis, reduced clinician autonomy, and weakened trust and therapeutic relationships.ConclusionsAI holds promise for enhancing triage, patient participation, and information exchange in mental healthcare. However, concerns about trust, safety, and overreliance on technology must be addressed. Future efforts should prioritize human-centric SDM, ensuring AI implementation mitigates risks related to equity, data privacy, and the preservation of therapeutic relationships.

Specialists in Poisons Information (SPIs), most of whom are pharmacists, work in Australian Poisons Information Centres (PICs) and provide telephone-based triage (tele-triage) and management advice for poison exposures. Australian PICs answer calls from the public and healthcare practitioners and are considered an emergency telephone service. While tele-triaging and clinical decision-making have been explored in other health professions, limited studies are available exploring how pharmacists apply their knowledge to make clinical decisions in a busy tele-triage emergency setting. To explore how SPIs apply clinical judgement in tele-triage and to understand the factors that shape their decision-making. This study represents the second, qualitative phase of an exploratory sequential mixed-methods design examining calls related to unintentional poisoning exposures in older adults (≥ 75years) to the New South Wales PIC. Semi-structured interviews with 12 SPIs were conducted, supported by clinical vignettes and analysed using an inductive approach. Thematic analysis was combined with process mapping to describe the decision-making process. SPIs followed a flexible, three-phase process of information gathering, risk stratification, and management decision-making. This process was iterative, shaped by experience, clinical knowledge, and the urgency of the call. Decision-making relied on the ability to balance known and uncertain risks, interpret caller information, and assess social and clinical context. While structured guidelines supported consistency, SPIs emphasised the importance of clinical autonomy, particularly in complex cases. A strong collegial culture and peer learning were central to developing decision-making skills. Time pressure and documentation requirements created tensions, highlighting the need to align workflows with clinical priorities. SPI decision-making is a dynamic, context-dependent process that combines clinical expertise, guideline use, and real-time judgement. Findings have implications for SPI training, documentation systems, and the design of telehealth services involving complex risk assessment.

The current Micrographic Surgery and Dermatologic Oncology (MSDO) fellowship match process requires applicants and fellowship directors to rank each other with limited information. Fellowship directors receive standardized information about applicants, but applicants lack comparable program data. To determine how MSDO applicants learn about fellowship programs, and what program characteristics influence their rank list decisions. All MSDO applicants to the University of Pennsylvania for the 2025 to 2026 fellowship year were invited to participate in a web-based survey distributed via email. The survey evaluated applicant characteristics, preferred information sources to learn about programs, program characteristics impacting applicants rank list, and which applicant achievements and personal characteristics they believed fellowship directors valued most. Data collection concluded when 20% of the estimated 2024 national MSDO applicant pool responded. This survey analyzed responses from 39 MSDO applicants (26% of the estimated national applicant pool). Applicants primarily relied on interviews (84.6%), program websites (82.0%), and mentors (71.8%) for information. The most valued program characteristics were clinical autonomy, complex case volume, and prior fellows' satisfaction. Fellowship directors can improve match outcomes by providing clear, standardized information through program websites, structured interviews, and details on clinical training, case complexity, and fellow autonomy.

Nurse practitioners (NPs) play a vital role in US nursing homes, providing medical management, improving care coordination, reducing hospitalizations, and addressing physician shortages. Understanding factors influencing NP job satisfaction is essential for workforce retention and sustaining quality care in these complex settings. To describe factors influencing job satisfaction among NPs working in US nursing homes. A descriptive, cross-sectional web-based survey was conducted between April and August 2024 using the Misener Nurse Practitioner Job Satisfaction Scale, a validated 44-item instrument based on Herzberg dual-factor theory. Participants were recruited through the Gerontological Advanced Practice Nurses Association and social media. The survey assessed satisfaction across six domains, including autonomy, collegiality, and professional growth. Among 157 participants, 75.1% held a master's degree and 22.9% a Doctor of Nursing Practice. The mean global satisfaction score was 4.4 (SD = 1.0) on a 6-point scale. Nurse practitioners reported the highest satisfaction with autonomy, challenge in work, and professional relationships with supervisors and colleagues. Areas of lower satisfaction included compensation for duties outside regular responsibilities, involvement in research, time for professional service, and administrative support. Nurse practitioners reported moderate to high satisfaction, especially with clinical autonomy and patient care. Addressing gaps in compensation, administrative support, and professional development could strengthen retention and improve care quality in nursing homes. Health care leaders should prioritize supportive work environments and expand professional growth opportunities to retain NPs and ensure quality care for aging populations.

BackgroundAcute Kidney Injury (AKI) is a common yet preventable complication among surgical patients, contributing to increased morbidity, prolonged hospital stays, and higher healthcare costs. Early detection is critical; however, the absence of a standardized nursing-led risk assessment tool for AKI limits proactive intervention in clinical practice.ObjectiveThis study aimed to develop and evaluate the Nursing Risk Assessment for Acute Kidney Injury tool, integrating the Fuzzy Logic Model (FLM) to enhance interpretive accuracy and improve nursing-led AKI risk detection and decision-making.MethodsA Design and Development Research (DDR) framework was employed in three phases. Phase 1 involved a needs analysis using a focus group discussion to explore the necessity of AKI assessment among surgical nurses. Phase 2 focused on tool development through expert consensus (surgeon, nephrologist, nursing academician, and experienced nurse) and evidence synthesis via a systematic literature review. In Phase 3, the Nursing Risk Assessment-AKI tool was evaluated through a quasi-experimental design at Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, involving 75 surgical nurses assessing 200 patients.ResultsPost-intervention analysis indicated increased nursing confidence, with 95.7% expressing positive perception of tool use. The FLM-supported tool demonstrated a predictive accuracy of 81.3%; however, the potential for false positives or negatives remains, especially given the single-center context. Fuzzy logic stratified patients into risk groups: at risk (33.5%), borderline (20.5%), and no risk (46.0%). ANOVA analysis revealed significant differences (p < 0.05) between AKI risk and factors such as age, gender, comorbidities, clinical/laboratory parameters, surgery types, and nephrotoxic agent usage.ConclusionWhile initial findings support the usability and clinical feasibility of the NURA-AKI tool, further multicenter validation is needed. The tool is designed to complement nurse judgment, promoting early AKI detection and structured risk communication in surgical care without replacing clinical autonomy.

What do European guidelines say about genetic testing for people with mental disorders? A scoping review

Nurses play a critical role in emergency departments, where timely and independent clinical decision-making is essential. However, their ability to make decisions is heavily influenced by the policy frameworks that govern their practice. This study aims to review and compare national policy frameworks that support nurses’ clinical decision-making in emergency care settings, with a focus on four countries. A structured policy review was conducted using six major databases and repositories, including PubMed, ScienceDirect, WHO IRIS, the Indonesian Ministry of Health, ICN, and PPNI. The search focused on documents published between 2014 and 2024. Following the PRISMA 2020 flow, four policy documents met the inclusion criteria and were selected for final synthesis, each representing Indonesia, Australia, the United Kingdom, and the United States. The findings reveal that while Indonesia has basic legal provisions for nursing roles, it lacks operational policies that enable clinical autonomy in emergency settings. In contrast, Australia, the United Kingdom, and the United States have more advanced frameworks that include protocol-based authority, standardized training, and structured interdisciplinary collaboration for emergency nurses. Strengthening Indonesia’s emergency nursing policies requires adopting best practices from countries with more developed systems. Expanding nurses’ clinical authority, formalizing competency-based training, and integrating decision-making protocols are essential to improve responsiveness and quality of care in emergency departments.

Background: Artificial intelligence (AI) shows strong potential to transform primary care by streamlining workflows, improving diagnostics, and enhancing patient outcomes. However, integration faces barriers, including PCPs’ concerns about workflow disruptions, reliability, and loss of human connection. This study explored PCPs’ perspectives and challenges around AI integration in primary care to inform the development of practical, human-centered tools. Method: This qualitative study included four focus groups (n = 40), comprising PCPs, residents, and AI developers, in December 2024. Sessions were recorded, transcribed, and analyzed using thematic analysis. Three main themes emerged: (1) From Frustration to Innovation: PCPs’ experiences with current technological gaps and their vision for improved support; (2) The Integration Paradox: tensions in embedding AI while safeguarding care quality; and (3) Beyond Basic Automation: future solutions that preserve clinical judgment. Result: Key findings emphasized the need for incremental AI adoption, starting with administrative tasks and progressing to clinical decision support, with systems acting as “silent partners” to enhance rather than replace human judgment. PCPs see AI as a promising way to reduce administrative burden and improve care quality but stress the need for human-centered design that protects the doctor–patient relationship. Conclusion: Successful integration requires addressing workflow compatibility, ethical concerns, and preserving clinical autonomy through collaborative development.

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care.

Managing multiple long-term conditions (MLTC) is complex. Clinical management guidelines are typically focused on individual conditions and lack a robust evidence base for patients with MLTC. MLTC management is largely delivered in primary care, where health care professionals (HCPs) have identified the need for more holistic yet efficient models of care that can address patients' medical, pharmacological, social, and mental health needs. Artificial intelligence (AI) has proven effective in tackling complex, data-driven challenges in various fields, presenting significant opportunities for MLTC care. However, its role in managing patients with multifaceted psychosocial needs remains underexplored. The implementation of AI tools in this context introduces opportunities for innovation and challenges related to clinical appropriateness, trust, and ethical considerations. Understanding HCPs' experiences of MLTC management and the factors influencing their attitudes toward using AI in complex clinical decision-making is crucial for successful implementation. We aimed to explore the perspectives of primary care HCPs on managing MLTC and their attitudes toward using AI tools to support clinical decision-making in MLTC care. In total, 20 HCPs, including general practitioners, geriatricians, nurses, and pharmacists, were interviewed. A patient case study was used to explore how an AI tool might alter the way in which participants approach clinical decision-making with a patient with MLTC. We derived concepts inductively from the interview transcripts and structured them according to the 5 categories of the model by Buck exploring determinants of attitudes toward AI. These included the concerns and expectations that contributed to the minimum requirements for HCPs to consider using an AI decision-making tool, as well as the individual characteristics and environmental influences determining their attitudes. HCPs' perspectives on managing MLTC were grouped into three main themes: (1) balancing multiple competing factors, including accounting for patients' social circumstances; (2) managing polypharmacy; and (3) working beyond single-condition guidelines. HCPs typically expected that AI tools would improve the safety and quality of clinical decision-making. However, they expressed concerns about the impact on the therapeutic clinician-patient relationship that is fundamental to the care of patients with MLTC. The key prerequisites for clinicians adopting AI tools in this context included improving public and patient trust in AI, saving time and integrating with existing systems, and ensuring that the rationale behind a recommendation is apparent to enable a final decision made by an experienced human clinician. This is the first study to examine the attitudes of HCPs toward using AI decision-making tools in the context of managing MLTC. HCPs were optimistic about AI's potential to improve decision-making safety and quality but emphasized that the human touch remains essential for patients with complex needs. We identified critical requirements for AI adoption, including addressing patients' perceptions, time efficiency, and the preservation of clinician and patient autonomy. RR2-10.1136/bmjopen-2023-077156.

BackgroundWith general practice becoming an increasingly important part of undergraduate medical education, it is vital to have reliable ways for assessing placement quality. The most prevalent tools for such assessment are the Dundee Ready Education Environment Measure (DREEM) and the Manchester Clinical Placement Index (MCPI). These instruments were not specifically designed for use only in GP contexts and were developed without student involvement at inception. As a result, they may omit quality indicators valued by students on GP placement. This study sought to understand the quality of learning in GP from the student perspective and compare this to what is assessed by DREEM and MCPI. We hope the results of this study will inform the development of placement quality questionnaires inspired by the student voice.MethodsPurposive sampling was used to recruit 17 undergraduate medical students in academic years 3–5 at the University of Bristol into four focus groups. These ran from May 2023 to April 2024 and were based on a topic guide developed and agreed on by all authors. Theoretical saturation was achieved as no new themes emerged in the final focus group. The focus groups were recorded and transcribed in full. The transcripts underwent systematic coding using NVivo. The codes formed the basis for the thematic analysis.ResultsFour main themes emerged: a sense of belonging, quality of learning, efficiency of learning, and the qualities of the GP teacher. These themes are illustrated by pertinent quotes from the focus groups. Subthemes included being given appropriate clinical autonomy within the placement, timely access to resources, structured clinical activities, and tutor qualities such as enthusiasm and leadership.ConclusionsThis paper offers a new perspective on the quality of GP placements by focusing on the student experience. It identifies themes and subthemes that education teams should assess, beyond what's covered by existing tools like MCPI and DREEM. These include students’ sense of belonging, perceived learning efficiency, and appropriate clinical autonomy. We argue that ignoring these areas neglects key aspects of student needs. We are piloting a new questionnaire incorporating these themes to better identify substandard placements and improve student experiences.

Introduction: Nurses' autonomy is a critical component of effective clinical practice, closely tied to professional competence, patient safety, job satisfaction, and organizational support. Despite its importance, autonomy remains variably defined and experienced across different healthcare systems and cultural contexts. This review explores the determinants, expressions, and implications of nurses’ autonomy based on an analysis of recent international studies.Methods: This research design uses a PRISMA-ScR in February 2024 based on PRISMA guidelines. Studies were taken from Esco, ScienceDirect, Google search, and PubMed and searched in English using the keywords nurse or nurses or registered nurse, clinical privilege or clinical autonomy or professional autonomy nurse, patient safety or quality of care from 2020 to 2024. Results: The findings reveal that nurses’ autonomy is positively associated with professional competence, role clarity, and supportive work environments. Autonomy enables nurses to participate in safety activities, advocate for patients, and contribute meaningfully to decision-making processes. Conversely, autonomy is often limited by hierarchical organizational structures, inadequate staffing, and a lack of managerial support. Studies also show that autonomy is context-dependent, with higher levels reported in critical care settings and among more experienced or specially trained nurses. Tools such as HSOPSC, SAQ, and NPC scales were commonly used to assess related variables.Conclusions: The review highlights that fostering nurses’ autonomy requires not only individual competence but also systemic change. Educational initiatives, leadership development, and non-punitive safety cultures are essential to empowering nurses.